FragFlex: Fragment-Based Flexible Molecular Generation for Exploring Vast Chemical Space

Abstract:Drug discovery requires efficient exploration of an astronomically large chemical space. Molecular graph diffusion models offer a promising approach to this task, but many require graph size to be fixed before generation. Although atom-level insertion and deletion can enable flexible-size generation, jointly modeling molecular size, atom types, and bonds while maintaining chemical validity remains challenging. We introduce FragFlex, a flexible-length discrete diffusion model that performs node insertion and deletion at the fragment level. During denoising, FragFlex jointly learns the size, fragment identities, and inter-fragment connectivity of the fragment graph. A neural attachment-site predictor and valence-aware assembler then convert the generated fragment graph into an atom-level molecule. In large-scale experiments generating 10 million molecules, FragFlex achieves near-perfect chemical validity while maintaining high uniqueness, demonstrating a strong balance between the two.

15 pages, 5 figures, 5 tables. Accepted at the NeurIPS 2026 Workshop on AI for Drug Discovery: Bridging the Translation Gap

Machine Learning (cs.LG)

arXiv:2610.15284 [cs.LG]

(or arXiv:2610.15284v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.15284 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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